Worker-Agent Integration Skill

Intelligent coordination between background workers and specialized agents.

Quick Start

# View agent recommendations for a trigger

npx agentic-flow workers agents ultralearn

npx agentic-flow workers agents optimize

# View performance metrics

npx agentic-flow workers metrics

# View integration stats

npx agentic-flow workers stats --integration

Agent Mappings

Workers automatically dispatch to optimal agents based on trigger type:

Trigger

Primary Agents

Fallback

Pipeline Phases

ultralearn

researcher, coder

planner

discovery → patterns → vectorization → summary

optimize

performance-analyzer, coder

researcher

static-analysis → performance → patterns

audit

security-analyst, tester

reviewer

security → secrets → vulnerability-scan

benchmark

performance-analyzer

coder, tester

performance → metrics → report

testgaps

tester

coder

discovery → coverage → gaps

document

documenter, researcher

coder

api-discovery → patterns → indexing

deepdive

researcher, security-analyst

coder

call-graph → deps → trace

refactor

coder, reviewer

researcher

complexity → smells → patterns

Performance-Based Selection

The system learns from execution history to improve agent selection:

// Agent selection considers:

// 1. Quality score (0-1)

// 2. Success rate

// 3. Average latency

// 4. Execution count

const { agent, confidence, reasoning } = selectBestAgent('optimize');

// agent: "performance-analyzer"

// confidence: 0.87

// reasoning: "Selected based on 45 executions with 94.2% success"

Memory Key Patterns

Workers store results using consistent patterns:

{trigger}/{topic}/{phase}

Examples:

- ultralearn$auth-module$analysis

- optimize$database$performance

- audit$payment$vulnerabilities

- benchmark$api$metrics

Benchmark Thresholds

Agents are monitored against performance thresholds:

{

  "researcher": {

    "p95_latency": "<500ms",

    "memory_mb": "<256MB"

  },

  "coder": {

    "p95_latency": "<300ms",

    "quality_score": ">0.85"

  },

  "security-analyst": {

    "scan_coverage": ">95%",

    "p95_latency": "<1000ms"

  }

}

Feedback Loop

Workers provide feedback for continuous improvement:

import { workerAgentIntegration } from 'agentic-flow$workers$worker-agent-integration';

// Record execution feedback

workerAgentIntegration.recordFeedback(

  'optimize',           // trigger

  'coder',              // agent

  true,                 // success

  245,                  // latency ms

  0.92                  // quality score

);

// Check compliance

const { compliant, violations } = workerAgentIntegration.checkBenchmarkCompliance('coder');

Integration Statistics

$ npx agentic-flow workers stats --integration

Worker-Agent Integration Stats

══════════════════════════════

Total Agents:       6

Tracked Agents:     4

Total Feedback:     156

Avg Quality Score:  0.89

Model Cache Stats

─────────────────

Hits:     1,234

Misses:   45

Hit Rate: 96.5%

Configuration

Enable integration features in .claude$settings.json:

{

  "workers": {

    "enabled": true,

    "parallel": true,

    "memoryDepositEnabled": true,

    "agentMappings": {

      "ultralearn": ["researcher", "coder"],

      "optimize": ["performance-analyzer", "coder"]

    }

  }

}